{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入pandas库，用于整理数据，并作建单的图表\n",
    "import pandas as pd\n",
    "\n",
    "#导入matplotlib库\n",
    "import matplotlib\n",
    "\n",
    "#导入pyplot模块\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#显示图表中的中文字符\n",
    "plt.rcParams['font.sans-serif'] =['SimHei'] # windows可设为SimHei，mac可设为Hiragino Sans GB\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### （1）读取CSV格式文件“task_2_lianjia_data.csv”，将数据命名为lianjia_data，并选取前部数据查看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>简介（方式·小区名 户型 朝向）</th>\n",
       "      <th>区</th>\n",
       "      <th>地铁站</th>\n",
       "      <th>村</th>\n",
       "      <th>面积大小（㎡）</th>\n",
       "      <th>朝向</th>\n",
       "      <th>户型</th>\n",
       "      <th>楼层类型</th>\n",
       "      <th>楼层（层）</th>\n",
       "      <th>标签</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>整租·长桥一村 1室0厅 南</td>\n",
       "      <td>徐汇</td>\n",
       "      <td>长桥</td>\n",
       "      <td>长桥一村</td>\n",
       "      <td>39</td>\n",
       "      <td>南</td>\n",
       "      <td>1室0厅1卫</td>\n",
       "      <td>高楼层</td>\n",
       "      <td>6</td>\n",
       "      <td>随时看房</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>整租·馨宁公寓 1室1厅 南</td>\n",
       "      <td>徐汇</td>\n",
       "      <td>华泾</td>\n",
       "      <td>馨宁公寓</td>\n",
       "      <td>42</td>\n",
       "      <td>南</td>\n",
       "      <td>1室1厅1卫</td>\n",
       "      <td>高楼层</td>\n",
       "      <td>29</td>\n",
       "      <td>精装,随时看房</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>整租·长桥三村 2室1厅 南</td>\n",
       "      <td>徐汇</td>\n",
       "      <td>长桥</td>\n",
       "      <td>长桥三村</td>\n",
       "      <td>51</td>\n",
       "      <td>南</td>\n",
       "      <td>2室1厅1卫</td>\n",
       "      <td>高楼层</td>\n",
       "      <td>6</td>\n",
       "      <td>随时看房</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>整租·东湾小区 1室1厅 南</td>\n",
       "      <td>徐汇</td>\n",
       "      <td>华泾</td>\n",
       "      <td>东湾小区</td>\n",
       "      <td>47</td>\n",
       "      <td>南</td>\n",
       "      <td>1室1厅1卫</td>\n",
       "      <td>中楼层</td>\n",
       "      <td>6</td>\n",
       "      <td>随时看房</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>整租·花苑村紫竹园 1室1厅 南</td>\n",
       "      <td>徐汇</td>\n",
       "      <td>康健</td>\n",
       "      <td>花苑村紫竹园</td>\n",
       "      <td>42</td>\n",
       "      <td>南</td>\n",
       "      <td>1室1厅1卫</td>\n",
       "      <td>高楼层</td>\n",
       "      <td>6</td>\n",
       "      <td>随时看房</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   简介（方式·小区名 户型 朝向）   区 地铁站       村  面积大小（㎡） 朝向      户型  \\\n",
       "0    整租·长桥一村 1室0厅 南  徐汇  长桥    长桥一村       39  南  1室0厅1卫   \n",
       "1    整租·馨宁公寓 1室1厅 南  徐汇  华泾    馨宁公寓       42  南  1室1厅1卫   \n",
       "2    整租·长桥三村 2室1厅 南  徐汇  长桥    长桥三村       51  南  2室1厅1卫   \n",
       "3    整租·东湾小区 1室1厅 南  徐汇  华泾    东湾小区       47  南  1室1厅1卫   \n",
       "4  整租·花苑村紫竹园 1室1厅 南  徐汇  康健  花苑村紫竹园       42  南  1室1厅1卫   \n",
       "\n",
       "                          楼层类型  楼层（层）       标签  \n",
       "0  高楼层                              6     随时看房  \n",
       "1  高楼层                             29  精装,随时看房  \n",
       "2  高楼层                              6     随时看房  \n",
       "3  中楼层                              6     随时看房  \n",
       "4  高楼层                              6     随时看房  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取CSV文件“task_2_lianjia_data.csv”\n",
    "f = open(\"task_2_lianjia_data.csv\",encoding=\"utf-8\") \n",
    "lianjia_data = pd.read_csv(f)\n",
    "\n",
    "# 显示链家数据\n",
    "lianjia_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### （2）利用groupby函数，以【面积大小（㎡）】字段进行groupby，统计各个面积下的【房源数量】情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>房源数量</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>面积大小（㎡）</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         房源数量\n",
       "面积大小（㎡）      \n",
       "7           1\n",
       "9           3\n",
       "10          1\n",
       "11          1\n",
       "12          1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 以【面积大小（㎡）】字段进行groupby，统计各个面积下面【房源数量】\n",
    "#  lianjia_squre = lianjia_data.groupby(\"面积大小（㎡）\")[\"简介（方式·小区名 户型 朝向）\"].count()\n",
    "lianjia_squre = lianjia_data.groupby(\"面积大小（㎡）\")[\"简介（方式·小区名 户型 朝向）\"].agg(房源数量=(\"count\"))\n",
    "# 旧版本写法：\n",
    "# lianjia_squre = lianjia_data.groupby(\"面积大小（㎡）\")[\"简介（方式·小区名 户型 朝向）\"].agg({\"房源数量\": \"count\"})\n",
    "lianjia_squre.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### （3）将第（2）问所统计数据，作折线图和条形图，并得出相关结论"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 108, 109, 110, 111, 113, 116, 118, 121, 123, 125, 126, 128, 129, 130, 131, 133, 137, 139, 143, 145, 151, 152, 155, 162, 171, 172]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[1, 3, 1, 1, 1, 1, 2, 5, 4, 1, 4, 1, 7, 2, 4, 4, 4, 6, 3, 7, 12, 16, 37, 11, 19, 21, 29, 38, 24, 18, 24, 11, 9, 22, 20, 15, 8, 16, 15, 10, 17, 9, 14, 10, 17, 8, 9, 10, 6, 3, 4, 3, 8, 9, 6, 5, 6, 3, 7, 2, 5, 4, 5, 4, 2, 3, 3, 2, 4, 2, 3, 3, 6, 3, 2, 1, 2, 1, 2, 2, 6, 4, 7, 3, 5, 2, 3, 9, 4, 2, 4, 3, 2, 2, 1, 4, 4, 2, 3, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "# 提取lianjia_squre的索引，即不同的面积大小，并转换成list，用作横坐标值\n",
    "lianjia_squre_index = lianjia_squre.index.tolist()\n",
    "print(lianjia_squre_index)\n",
    "\n",
    "print(\"-\"*100)\n",
    "\n",
    "# 提取lianjia_squre【房源数量】的数据值，命名为lianjia_count，用作纵坐标值\n",
    "lianjia_squre_count = lianjia_squre[\"房源数量\"].values.tolist()\n",
    "print(lianjia_squre_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 调用plt，作趋势图（即折线图）\n",
    "# plt.plot（横坐标数值，纵坐标数值，线的类型，颜色，线宽，标记点类型，标记大小）\n",
    "plt.plot(lianjia_squre_index,lianjia_squre_count,'-.',  color='b',linewidth=2, marker='o',markersize=2)\n",
    "\n",
    "# 设置图片标题\n",
    "plt.title(\"链家房源面积分布情况\")\n",
    "# 设置图片横轴标签\n",
    "plt.xlabel(\"面积大小\")\n",
    "# 设置图片纵轴标签\n",
    "plt.ylabel(\"房源数量\")\n",
    "# 设置图片大小\n",
    "plt.rcParams['figure.figsize'] = (25,15)\n",
    "# 解决中文显示问题\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # windows可设为SimHei，mac可设为Hiragino Sans GB\n",
    "# 保存图片为\n",
    "plt.savefig(\"plot.png\")\n",
    "\n",
    "# 展示图片\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 调用plt，作条形图\n",
    "# plt.bar（横坐标数值，纵坐标数值，条形分布类型，颜色，条形透明度）\n",
    "# plt.bar(lianjia_squre_index,lianjia_count,align=\"center\",color=\"r\",alpha=0.7)\n",
    "plt.bar(lianjia_squre_index,lianjia_squre_count,align=\"center\",color=\"r\",alpha=0.7)\n",
    "\n",
    "# 设置图片标题\n",
    "plt.title(\"链家房源面积分布情况\")\n",
    "# 设置图片横轴标签\n",
    "plt.xlabel(\"面积大小\")\n",
    "# 设置图片纵轴标签\n",
    "plt.ylabel(\"房源数量\")\n",
    "# 设置图片大小\n",
    "plt.rcParams['figure.figsize'] = (25,15)\n",
    "\n",
    "# 保存图片为\n",
    "plt.savefig(\"bar.png\")\n",
    "\n",
    "# 展示图片\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barh为横条形图\n",
    "plt.barh(lianjia_squre_index,lianjia_squre_count,align=\"center\",color=\"r\",alpha=0.5)\n",
    "# 保存图片\n",
    "plt.savefig(\"barh.png\")\n",
    "# 展示图片\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 【结论】链家所出租的房屋面积多集中在25-50㎡阶段"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### （4）利用groupby函数，以【区】字段进行groupby，统计各个上海各个“区”所属的【房源数量】情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>房源数量</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>区</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>奉贤</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>嘉定</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>青浦</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>普陀</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>虹口</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>松江</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>杨浦</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>黄浦</th>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>长宁</th>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>闵行</th>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>浦东</th>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>静安</th>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>徐汇</th>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    房源数量\n",
       "区       \n",
       "奉贤     1\n",
       "嘉定     6\n",
       "青浦    10\n",
       "普陀    22\n",
       "虹口    24\n",
       "松江    45\n",
       "杨浦    56\n",
       "黄浦    76\n",
       "长宁    86\n",
       "闵行    86\n",
       "浦东   106\n",
       "静安   108\n",
       "徐汇   126"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 以“区”字段进行groupby，统计各个区域的【房源数量】\n",
    "lianjia_district=lianjia_data.groupby(\"区\")[\"简介（方式·小区名 户型 朝向）\"].agg(房源数量=(\"count\")).sort_values(by=\"房源数量\")\n",
    "lianjia_district"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### （5）将第（4）问所统计数据，作条形图，并得出相关结论"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['奉贤', '嘉定', '青浦', '普陀', '虹口', '松江', '杨浦', '黄浦', '长宁', '闵行', '浦东', '静安', '徐汇']\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[1, 6, 10, 22, 24, 45, 56, 76, 86, 86, 106, 108, 126]\n"
     ]
    }
   ],
   "source": [
    "# 提取lianjia_district_DataFrame的索引，用作横坐标值\n",
    "lianjia_district_index = lianjia_district.index.tolist()\n",
    "print(lianjia_district_index)\n",
    "\n",
    "print(\"-\"*100)\n",
    "\n",
    "# 提取lianjia_squre_DataFrame的【房源数量】的值，即lianjia_district,用作纵坐标值\n",
    "lianjia_district_count = lianjia_district[\"房源数量\"].values.tolist()\n",
    "print(lianjia_district_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 调用plt，作条形图\n",
    "# plt.bar（横坐标数值，纵坐标数值，条形分布类型，颜色，条形透明度）\n",
    "plt.bar(lianjia_district_index,lianjia_district_count,align=\"center\",color=\"r\",alpha=0.7)\n",
    "\n",
    "# 设置图片标题\n",
    "plt.title(\"链家区域房源分布情况\")\n",
    "# 设置图片横轴标签\n",
    "plt.xlabel(\"区域\")\n",
    "# 设置图片纵轴标签\n",
    "plt.ylabel(\"房源数量\")\n",
    "# 设置图片大小\n",
    "plt.rcParams['figure.figsize'] = (25,15)\n",
    "\n",
    "# 保存图片为\n",
    "plt.savefig(\"bar_2.png\")\n",
    "\n",
    "# 展示图片\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 【结论】徐汇区房源数量最多，奉贤区最少"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### （6）利用groupby函数，以【楼层类型】字段进行groupby，统计各个楼层所属的【房源数量】情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>房源数量</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>楼层类型</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中楼层</th>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>低楼层</th>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>地下室</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>高楼层</th>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             房源数量\n",
       "楼层类型                             \n",
       "中楼层                           231\n",
       "低楼层                           146\n",
       "地下室                             5\n",
       "高楼层                           370"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 以【楼层类型】字段进行groupby，统计各种楼层【房源数量】\n",
    "lianjia_floor = lianjia_data.groupby(\"楼层类型\")[\"简介（方式·小区名 户型 朝向）\"].agg(房源数量=(\"count\"))\n",
    "lianjia_floor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 将第（6）问所统计数据，作饼图，并得出相关结论"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['中楼层                        ', '低楼层                        ', '地下室                        ', '高楼层                        ']\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[231, 146, 5, 370]\n"
     ]
    }
   ],
   "source": [
    "# 提取lianjia_floor_DataFrame的索引，用作横坐标值\n",
    "lianjia_floor_index = lianjia_floor.index.tolist()\n",
    "print(lianjia_floor_index)\n",
    "\n",
    "print(\"-\"*100)\n",
    "\n",
    "# 提取lianjia_floor_DataFrame的【房源数量】的值，即llianjia_floor,用作纵坐标值\n",
    "lianjia_floor_count = lianjia_floor[\"房源数量\"].values.tolist()\n",
    "print(lianjia_floor_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JUgNZ9iRJkiSpgSx7kiRJktRAlj1JkiRJaiDLniRJkiQ1kGVPkiRJkhrIsidJkiRJDWTZkyRJkqQGsuxJkiRJUgNZ9iRJkiSpgSx7kiRJktRAlj1JkiRJaiDLniRJkiQ1kGVPkiRJkhrIsidJkiRJDWTZkyRJkqQGsuxJkiRJUgNZ9iRJkiSpgSx7kiRJktRAlj1JkiRJaiDLniRJkiQ1kGVPkiRJkhrIsidJkiRJDWTZkyRJkqQGsuxJkiRJUgNZ9iRJkiSpgSx7kiRJktRAlj1JkiRJaiDLniRJkiQ1kGVPkiRJkhpoSuoAkqTmylrtIaovFofHfp17z5188tI3DwGjE12KkXI0VV5JkprEsidJulfWak8F5teXeRvx60yqYjdW7u5j2ujya4Gt1pahzAuApcAdwO0T/DrRbeN/vQ24HrimGCnv2IiPQZKkRrDsSdIAyFrtecCOE1y2AzZn9bLWC2bUl0Wb8iJlXtwBXLOuSzFS3rRJaSVJ6kGWPUlqgKzV3pJVBS7jvqVuXrJwac0Bdqsva1XmxT3AP6nK3z+APwC/B/4P+H0xUv6zwzklSZp0lj1J6gP1uW+7A/tQFZfxRW4HemdErl9NZ9XneR9lXtxGVf7GCuD/jf2+GClv6VZISZI2hGVPknpMPeVyH+CB4y57YaFLaS6wb31ZTZkXN7B6Afw18NNipPx7VxNKkrQGy54kJZK12gHYhdVL3QNZy+iSetai+vLI8TeWeXEd8DPgJ8BPqQrgn7sfT5I0qCx7ktQFWas9h1WjdWO/7k11TpmaaUvg8fUFgDIvbmKNAgj8oRgpY5KEkqRGs+xJUgdkrfaOwMH15UBgVyAkDaVesAB4TH0Zc2uZFz+nKn4/Br7tFFBJ0mSw7EnSJMha7Z1ZVe6W4FRMrb95rPq7A0CZF38CrgC+BXyrGCn/miaaJKmfWfYkaSNkrfburPoB/WCq/eqkybJzfTkeoMyLv7J6+ftTumiSpH5h2ZOk9ZC12nuwernbOm0iDZgdgefXF8q8uIq6+AFXFCPlHxJmkyT1KMueJE0ga7X3Ah5NVewOArZIm0hazfbAc+oLZV5cTTXydynwv8VIeVO6aJKkXmHZkyQga7WHqRZSORx4KpAlDSRtmMXAs+rLyjIvvg98GfhSMVL+NmkySVIylj1JAytrtWcBT6Aqd08CFqZNJE2KYeCA+vK2erGXL9eXbxUj5bKU4SRJ3WPZkzRQslZ7C+ApVAXvEGBm2kRSx+0MvKK+3F7mxVeBL1FN97w+aTJJUkdZ9iQ1XtZq70o1PfNwYH9gKG0iKZnNgCPqy2iZFz9k1XTPXyVNJkmadJY9SY2TtdoBeCjV6N3hwB5pE0k9aYjqy4/9gbeUeVECHwc+VoyUf0yaTJI0KUKMMXUGSdpkdcF7NHAU1TTNxWkTaW0W3X3LtR+57KytUufQ/foR8DHgE8VIeW3qMJKkjePInqS+lrXaOwHHUu0/tmPaNFJjPKy+vKPMi29QFb/PFiPlbWljSZI2hGVPUt/JWu3ZwJHAcVR74IW0iaTGGgYeW18+UOZFm6r4tYuR8p6kySRJ62TZk9Q3slb7AKqC9wxgTuI40qCZwarFXW4t8+IzVMXvm8VIOZo0mSRpQpY9ST0ta7W3o5qi+Xxgt8RxJFXmAcfXl3+UefEh4IJipPx72liSpPFcoEVSz8la7RlUq2geR7UXnlslNIgLtDTWSqANnAdc5mifJKXnyJ6knpG12g+lKnjPBOYnjiNpwwxTrYT7FODPZV5cCHywGCmvSxtLkgaXI3uSkspa7fnACVQlb8/EcdQFjuwNlGXA54DzipHyisRZJGngOLInKYl6y4RXUp3z42IrUjNNA44Gji7zYgQ4H/hwMVLenDaWJA0GR/YkdVXWaj8COAV4GtW0Lw0YR/YG3t3Ap6hG+36QOowkNZkje5I6Lmu1h6gWXDkFeGTiOJLSmkm9wm6ZFz8E/gP4vAu6SNLks+xJ6ph68/PjqKZr7pI4jqTe83DgM8D/lXlxDvARN2uXpMlj2ZM06bJWezHwcuDFwOaJ40jqfbsDFwJnlnnxHuADxUh5W+JMktT3LHuSJk3Wau9DNVXzGKqFGSRpQ2wDvA14fZkX5wHvLkbKaxJnkqS+ZdmTtMmyVvsJVCXvkNRZJDXCXOC1wMllXnwEOKcYKf8vcSZJ6juWPUkbJWu1pwDPAU7F/fEkdcZ04AXA8WVefAE4uxgpf5g4kyT1DcuepA2StdrDVCXvTbjoiqTuGKLaruVpZV5cCZxVjJRfS5xJknqeZU/Seqm3TzgaeDPwgMRxJA2ug4CvlnlxBfCGYqT8XuI8ktSzLHuS7lfWagfg6cAZOF1TUu9YAny3zIs2Ven7ReI8ktRzhlIHkNS7slb7MOCnwCVY9CT1picBPyvz4hNlXuyWOowk9RJH9iTdR9ZqPxr4d6oNjyWp1wWqaeZHlHnxYeCMYqS8KnEmSUrOsifpXlmr/SCqPa4enzqLJG2EKcAJwHPqffreUoyU1yfOJEnJWPYkkbXaOwFnAc+k+oZckvrZdOBk4IQyL95DtU/frYkzSVLXWfakAZa12ltQbaHwYmBa4jiSNNnmAG8ATirz4j+A9xQj5d2JM0lS17hAizSAslZ7dtZqvxn4I/ByLHqSmm1zqvOQR8q8OCp1GEnqFsueNGCyVvsoYAQ4HdgsbRpJ6qodgE+VefGNMi/2Sh1GkjrNaZzSgMha7d2Bc4HHps4iSYk9Gvh5mRcfAP61GClvTh1IkjrBsic1XNZqzwLeCJyC0zUlacww8DLgmDIv3ghcWIyUo4kzSdKkchqn1GBZq/004LfA67DoSdJEFgHnAT8u8+KRqcNI0mRyZE9qoKzV3hV4L/DE1FkkqU88BPhumRcfBV5bjJRXpw4kSZvKsic1SNZqz6AaxTuNap8pSdKGeTbw1DIv3gK8sxgpl6UOJEkby2mcUkNkrfaTqaZs/isWPUnaFHOotmr4TZkXh6YOI0kby5E9qc9lrXZGNWXzsMRRJKlpdgXaZV58AnhFMVJenzqQJG0IR/akPpW12tOzVvtNVKN5Fj1J6pxjgN+WefHM1EEkaUNY9qQ+lLXaTwB+DZwJzEwcR5IGwSLgY2VefLHMi8Wpw0jS+rDsSX0ka7XnZa32xcClVNOLJEnddRjVKN8JqYNI0rpY9qQ+kbXajwF+BTw3dRZJGnDzgP8q8+JrZV5kqcNI0tq4QIvU47JWeyZwNvAyICSOI0la5RDg12VevA44txgpY+pAkjSeI3tSD8ta7YcCPwVejkVPknrRbKoVkb9d5sUDUoeRpPEc2ZN6UNZqTwHeBLwe/zuVpH7wKODnZV6cAZxTjJQrUweSJEf2pB6Ttdo58H2qzdEtepLUP2ZQbcb+wzIv8tRhJMmyJ/WIrNUOWat9MvAzYL/UeSRJG21f4CdlXrwgdRBJg82yJ/WArNXeHvg68G6qb4YlSf1tFnBhmRefKvNifuowkgaTZU9KLGu1n0e1pcK/pM4iSZp0R1Gdy/fI1EEkDR7PB5ISyVrtRcD5wNNTZ5EkddSOwJVlXpwJvMXFWyR1iyN7UgJZq30Y8GssepI0KIaBM4BvlHmxfeowkgaDZU/qoqzVnpa12ucCXwS2Sp1HktR1BwG/KPPCL/skdZxlT+qSrNXeFvgW8NLUWSRJSW0OfKbMi/PKvJiZOoyk5rLsSV2QtdpLgJ8Cj0gcRZLUO14M/L8yL/ZOHURSM1n2pA7LWu3XUG2rsGXqLJKknrMH8KMyL05KHURS87gap9QhWau9GXARcETqLJKknjYDeF+ZFw8DTixGyqWpA0lqBkf2pA7IWu0C+BEWPUnS+ns+8G1X65Q0WSx70iTLWu0jqYpenjqLJKnv7Ed1Ht9BqYNI6n+WPWmSZK32cNZqvx34NDAndR5JUt/aEri8zIuXpw4iqb9Z9qRJkLXaW1ItwnJK6iySpEaYAry3zIuLyryYnjqMpP5k2ZM2UdZq70+1rcKSxFEkSc1zLNV5fNulDiKp/1j2pE2Qtdovo9oofdvUWSRJjfVQ4CdlXhyYOoik/uLWC9JGyFrtmcD5wHNTZ5EkDYSx8/heVYyU70sdRlJ/cGRP2kBZq70t8D0sepKk7poKnFvmxYc8j0/S+rDsSRsga7X3BL4PPCh1FknSwDoOuLLMi21SB5HU2yx70nrKWu2Dge8AbnYrSUrtYcD3y7xwT1dJa2XZk9ZD1mofA3wVmJ86iyRJtR2B75V5cUDqIJJ6k2VPWoes1X4N8DFgWuoskiStYXPga2VeHJk6iKTeY9mT1iJrtYeyVvu9wH8AIXUeSZLWYgbwyTIvTk4dRFJvsexJE8ha7RnAp4GXp84iSdJ6GALeXebFO8q88AtKSYBlT7qPrNVeCFwOPD11FkmSNtCrgY+7NYMksOxJq8la7Z2A7wKPTJ1FkqSNdDRwWZkXLiomDTjLnlTLWu19qfbQe0DqLJIkbaKDge+WebFD6iCS0rHsSUDWaj8RuALYKnEUSZImyx5Ue/E9MHUQSWlY9jTwslb7eOCLwJzUWSRJmmSLgSvLvDgkdRBJ3WfZ00DLWu3TgQ8CUxJHkSSpU+YC/+tefNLg8QdcDaSs1Q7A+4ETU2eRJKkLpgKfKPPi2GKk/J/UYSR1hyN7Gjh10Tsfi54kabAMAx8u8+KFqYNI6g7LngbKuKLnP3SSpEE0BFxQ5sXJqYNI6jzLngaGRU+SpHu9u8yL16cOIamzLHsaCHXRuwCLniRJY95S5sVZqUNI6hzLnhqvLnoXAi9InUWSpB7zhjIv/j11CEmdYdlTo9VF77+AE1JnkSSpR7XKvDg7dQhJk8+yp8bKWu0hqqJ3fOoskiT1uNeWeXFO6hCSJpdlT41k0ZMkaYOdWubFO1OHkDR5LHtqnLrofRA4LnUWSZL6zKvKvHh36hCSJodlT40yrugdmziKJEn96uQyL96ROoSkTWfZU2PURe9DWPQkSdpUry7z4g2pQ0jaNJY9NUJd9C4Cnp86iyRJDXFWmRcnpQ4haeNZ9tT3xhW956XOIklSw5xb5sWzUoeQtHEse2qCD2DRkySpEwLw4TIvnpQ6iKQNZ9lTX8ta7X8FXpQ6hyRJDTYF+HSZFwemDiJpw1j21LeyVvt44IzUOSRJGgAzgS+VefHg1EEkrT/LnvpS1mo/ETg/dQ5JkgbIPOArZV7snjqIpPVj2VPfyVrt/YBPU00rkSRJ3bMl8LUyL7ZLHUTSuln21FeyVntnoA3MTp1FkqQBtQNV4VuUOoik+2fZU9/IWu1FwFeovlWUJEnp5FRTOjdLHUTS2ln21BeyVnsW8GVgt9RZJEkSAPtSLdoyI3UQSROz7KnnZa32MPBJ4OGps0iSpNUcDFyUOoSkiVn21A/eDzw5dQhJkjShY8q8eHPqEJLuy7Knnuam6ZIk9YXTy7w4JnUISauz7KlnuWm6JEl95aIyLzzlQuohlj31JDdNlySp78wAvlDmxQ6pg0iqWPbUc9w0XZKkvrUV8GW3ZJB6g2VPPcVN0yVJ6nt7Ax8r88KfM6XEJv0/wlAZXo/HTQsh3Of96+dPn+xc6n1Zqz0b+AJumi5JUr97MvD21CGkQbdR0+RCCCcDIzHGyya4e0fg2BDC24BXA58HDgN+B8QY4xfqx70X2CuEsGKC19gayDcmm/rafwF7pQ4hSZImxavKvBgpRsoLUgeRBtV6l70QwpeAsfnXi4G7Qgivq6/fHmM8LIRwHPBSqpGZbYD9gacBC4A7gVtCCCtijG1gOnBhffuaTtiYg1H/ylrtVwIu2SxJUrO8r8yLPxYj5eWpg0iDaENG9u6KMR4GEEI4kWpk74r6+ifqx/w3cCvwDOAkYCfgjVTl73PAB4FQP3Ye8HtgORDHvU8ATt/gI1Hfylrtg4BzUueQJEmTbgpwSZkXjyhGyt+lDiMNmg0pe3GdD4gxhhD2A7YF3gEMA+8EDgWuAS4G3gT8GRiOMX4vhHDFGi8TgG8CP9yAbOpTWau9LfApXHlTkqSmmk+1QufDi5HyptRhpEGyIT9g7zOumI1N47ylvr4lQAhhC2Au8FXg48AfgTOBD8QYrw4hfAcYO0fvnnohlkfHGFcrkiGEocrT9dAAACAASURBVBDCtBjjso05KPWHrNWeRrXFwlaps0iSpI7aFfhomReHFiPlOgcQJE2ODSl77Rjja2DCaZxjU/BeSjVKcxDwVmAXYA6wfwhhb6AE3kW14uLK+vd7hhB2A64G7qAa2QtUU0I/tAnHpt73LqrzOiVJUvM9ger0nn9LHUQaFOu99cJY0VvHfe8B/lT//hjgeuB5wIuAS2KMS2KMXwghFMCtMcaTgNOoVuZ8KNWqnd8EHhtjtOg1WNZqP5/qvE5JkjQ4Ti/z4jGpQ0iDYmP32RsbfVtNjPHmsftijKPAS6jO2bsU+OG4/feOBT4UQjgK+BrwSOAiqimfK4EzQghTNzKbelzWaj8YOC91DkmS1HVDVBuuL04dRBoEG7soxhxg9lrumwlsFkI4j2qlzdcD3waOBD5Wb8/wfuBvwC+BRTHGe8a/QAhhH6qtGZZvZD71qKzVXgB8FpiROoskSUpiS+ATZV78SzFSTrTfsqRJEtZYG0XqmKzVHgL+F3h86iyS0ll09y3XfuSys1yYSdI5xUj52tQhpCbb2Gmc0sY4E4ueJEmqnFrmxVNTh5CazLKnrsha7adQTemVJEmCevX1Mi92Sh1EairLnjoua7V3Ay5mgkV9JEnSQJsPXFLmxfTUQaQmsuypo7JWezbwOWBe6iySJKknPYRq+y5Jk8yyp047H9gzdQhJktTTXlzmxbNTh5CaxrKnjsla7WcB/o9bkiStj/PLvNgjdQipSSx76ois1d6Raj9FSZKk9TGb6vy9mamDSE1h2dOkq/fTuxjP05MkSRumAM5OHUJqCsueOuE04KDUISRJUl96WZkXh6QOITWBZU+TKmu19wPOSJ1DkiT1rQBcVObF/NRBpH5n2dOkqbdZ+CgwNXUWSZLU17YDzk0dQup3lj1NpncCu6cOIUmSGuHZZV4cmTqE1M8se5oUWat9GPCi1DkkSVKjnFfmxdapQ0j9yrKnTZa12guBC1LnkCRJjbMQ+GDqEFK/suxpMrwP8Fs3SZLUCYeWeeHsIWkjWPa0SbJW+xnA0alzSJKkRntnmRe7pA4h9RvLnjZa1mpvBbw/dQ5JktR4s4EPl3nhz67SBvA/GG2KC6jm0kuSJHXao4DXpg4h9RPLnjZK1mo/H3hK6hySJGmgnFHmxQNTh5D6hWVPGyxrtbcD3pM6hyRJGjjTgIvLvJiSOojUDyx72hgXAvNSh5AkSQNpH+DVqUNI/cCypw1Sr775hNQ5JEnSQHtzmRdZ6hBSr7Psab1lrfYc4J2pc0iSpIE3Czg3dQip11n2tCHOALZNHUKSJAl4UpkXR6QOIfUyy57WS9Zq7wW8InUOSZKkcd5T5sVmqUNIvcqyp/X1fsCVryRJUi/ZFjgrdQipV1n2tE5Zq/084MDUOSRJkibw0jIv9k0dQupFlj3dr6zVng+ckzqHJEnSWgwD55V54c+10hr8j0Lr8hZgy9QhJEmS7sd+wEtTh5B6jWVPa5W12vsCJ6bOIUmStB7OKvNiceoQUi+x7GlCWas9RLUoi39HJElSP5gLvCd1CKmX+IO81uaFwMNSh5AkSdoAR5Z5cWjqEFKvsOzpPrJWexHw1tQ5JEmSNsL7yryYmTqE1Asse5rI2cCC1CEkSZI2QgackjqE1Asse1pN1mo/EjgudQ5JkqRNcFqZF1ulDiGlZtnTvbJWe5hqUZaQOoskSdImmAOckTqElJplT+O9DHhg6hCSJEmT4AVlXuyROoSUkmVPAGSt9hbAmalzSJIkTZJh4JzUIaSULHsa8waq/WkkSZKa4tAyLw5JHUJKxbInslZ7B+DE1DkkSZI64O1lXvgzrwaSf/EF8GZgeuoQkiRJHfBA4HmpQ0gpWPYGXNZq58DzU+eQJEnqoLPKvJiVOoTUbZY9/RvVCcySJElNtS1utK4BZNkbYFmrvS9wROockiRJXfDaMi+2Th1C6ibL3mB7K26gLkmSBsMc3GZKA8ayN6CyVnsJ8LjUOSRJkrro+DIv9kwdQuoWy97gemvqAJIkSV02DJydOoTULZa9AZS12ocB+6fOIUmSlMCTyrx4aOoQUjdY9gZM1moPAW9JnUOSJCmhf00dQOoGy97geSawd+oQkiRJCT25zIuHpA4hdZplb4BkrfZUXIVKkiQJHN3TALDsDZYXADunDiFJktQDnlrmxYNSh5A6ybI3ILJWexbwptQ5JEmSeoije2o0y97geDmwTeoQkiRJPeTwMi9cy0CNZdkbAFmrPQ94beockiRJPSbg6J4azLI3GE4EFqQOIUmS1IOOKPNiz9QhpE6w7DVcvQLnK1LnkCRJ6lEB1zVQQ1n2mu9ZwOLUISRJknrYUWVeFKlDSJPNstd8p6QOIEmS1OOGcHRPDWTZa7Cs1X484ApTkiRJ63Z0mRcPSB1CmkyWvWZzVE+SJGn9DAGvTx1CmkyWvYbKWu19gMemziFJktRHjinzwn2J1RiWveY6NXUASZKkPjMNOCl1CGmyWPYaKGu1twWOSZ1DkiSpD724zIvpqUNIk8Gy10wnA1NTh5AkSepDWwDPTh1CmgyWvYbJWu3NgBelziFJktTHTk4dQJoMlr3meQEwL3UISZKkPrZPmRePTh1C2lSWvQbJWu0p+E2UJEnSZHhl6gDSprLsNctRwI6pQ0iSJDXAk8u82CV1CGlTWPaaxU3UJUmSJscQ8PLUIaRNYdlriKzVXgLsmzqHJElSgxxf5sXc1CGkjWXZaw43UZckSZpcmwHHpw4hbSzLXgNkrXYOHJo6hyRJUgO9vMwLf2ZWX/IvbjO8EAipQ0iSJDXQzsBhqUNIG8Oy1+eyVnsq8JzUOSRJkhrMra3Ulyx7/e9JwJapQ0iSJDXYo8u82C11CGlDWfb6nycNS5Ikdd5xqQNIG8qy18eyVntr4Impc0iSJA2A55V5MZw6hLQhLHv97bnAlNQhJEmSBsC2wONSh5A2hGWvvzmdQJIkqXv82Ut9xbLXp7JWe3+gSJ1DkiRpgDy1zIuFqUNI68uy17/8ZkmSJKm7pgHPSh1CWl+WvT6UtdqzgKNT51B/GV2+lBhH770eR1cSVyzfqNca/zqSJA0YV0JX33Bxj/50JDA3dQilccevLueOX1xGHF3B0LSZLDz0VQzP2Zybvn4BK276B6PL7mTufocze4+DV3vezV+/gOU3/QNCAGDl7TcwqziIzQ96Hstv/Ds3fe084op7iCuWMf/gY5m504O5s/w2t37/k4ThKSx68ilMXbg9ALd+52PMedATmbKZM1kkSQPnQWVePKgYKX+eOoi0Lpa9/uQUzgE1uuxulv7tV2z1zLcQhqdyZ3klt/3oswzN3Ixpi3Zg4eNeQlyxjGs/fTrTtt6VqQu2vfe5C5/4int/H0dXct0lZzJ3v6cCcPM3/ov5S45l+ta7svKuW/nnR09j8Qs+wJ2/+QbbHPseVtx8Dbf//FIWPOaF3DnyHaYs2NaiJ0kaZMcBJ6cOIa2L0zj7TNZq7wwcvM4HqpGGps1k0ZNeSRieSoyRFbf8kynzt+bu3/+AOQ98PABhyjRm7fZwll71m7W+zh2/uIzZ+aMYnjWPGCOz93oM07fetXqPGXMIU6bd+9i4fCkr776NMDyV5TdexbJr/8CcPR/d2QOVJKm3PbvMi2nrfpiUlmWv/xwHhNQhlNaNX/0AV1/wQpZd92c2e9ATIAwRhlcN1E/bahdW3HjVhM9defft3Fleyey9HgNACIHZxYEAjN5zFzd84WzmPfzphBCY+/AjuP6L/8HtP/kSc/Z+DLf+8DOM3nMX11x8Cnf/6SedP1BJknrTQuApqUNI6+I0zj6StdpDwPNT51B6Cx/3EmKM3PW773LT5RcyNH3maveHoWHi6IoJn3vnry9n1gMeRRgaXu32Zdf9iZsuv5D5Bz6PGdtVu3rM2H4vZmy/FzFGbv76+czc5aHcc9Vv2PIZZ3DDF89h5s77duYAJUnqfccDl6QOId0fR/b6yyHA9qlDKJ04upLRZXcD9YhcfgDLr/8LDE8lrly1subyG//OlLlbTvgad/zq68zOD1zttmXX/pGbLns/iw591b1Fb7zbf/plZu72CFi5ghk77sPwjDkMzZg9eQcmSVL/eVyZF4tTh5Duj2Wvv7jU74Bb+uefcf1nz7p3y4Slf/8tQ7PmMX2bB3D7z78CQFyxnDt+fTmz8kfd5/kr77qVuHwpw7Pnr3b7TV87n4WHncqUefctiEv//hviimXMzB7E8JwFLLv+L8SVKxhdekcHjlCSpL4xDDw3dQjp/jiNs09krfbmwOGpcyitGTvvy7Lr/8w1Hz6ZoRmbEabOYOHjT2J49ubc+NUP8M+PnArDU5i771OYMndLVt5xMzddfgFbPPU0AJZd83umbbXraq+58q5bWXbdn7jxf999721xxXK2eNrrGZ45l7vKK9n8kBcDMH37Pbntx5/nmoteztyHH9m9A5ckqTcdBZydOoS0NiHGmDqD1kPWah8PfDB1DjVPjKMQ473n8MUYYXQFDA0TwhAxRkJwTSBNnkV333LtRy47a6vUOSRpkuxUjJR/SR1CmojTOPvH01IHUDOFMLTaYi0hBMLwVEIYuve6JElaqyNSB5DWxrLXB7JWew7V4iySJEnqLZ7XoJ5l2esPTwBmpA4hSZKk+3h4mRfbpg4hTcSy1x+cwilJktSbAk7lVI+y7PW4rNWeChyaOockSZLWyrKnnmTZ631LgPnrepAkSZKSOaDMC1cZVs+x7PU+p3BKkiT1tiHg6alDSGuy7PWwrNUOwFNS55AkSdI6OZVTPcey19seCri6kyRJUu87uMyLRalDSONZ9nqbUzglSZL6wxTgqalDSONZ9nrb4akDSJIkab25wbp6imWvR2Wtdg7kqXNIkiRpvT2mzAtXUVfPsOz1Lkf1JEmS+stU4MmpQ0hjLHu9y7InSZLUfx6fOoA0xrLXg7JWezHwsNQ5JEmStMEOKfMipA4hgWWvVz0V8H8SkiRJ/WdrYJ/UISSw7PUqt1yQJEnqX49LHUACy17PyVrtecCS1DkkSZK00TxvTz3Bstd7DqZayUmSJEn96YAyL2amDiFZ9nrPktQBJEmStEmmU32BLyVl2es9S1IHkCRJ0ibzvD0lZ9nrIVmrPR94YOockiRJ2mSWPSVn2estB+KfiSRJUhPsWebF4tQhNNgsFr1lSeoAkiRJmjSO7ikpy15vWZI6gCRJkiaNWzAoKctej6j313tQ6hySJEmaNIeUeRFSh9Dgsuz1Ds/XkyRJapZFwENSh9Dgslz0jiWpA0iSJGnSHZI6gAaXZa93LEkdQJIkSZPuUakDaHBZ9npA1mrPxfP1JEmSmugRqQNocFn2esOBwHDqEJIkSZp0W5R5sWvqEBpMlr3esCR1AEmSJHXM/qkDaDBZ9nrDktQBJEmS1DGWPSVh2UusPl/vwalzSJIkqWMse0rCspfeAXi+niRJUpPtXebFnNQhNHgse+ktSR1AkiRJHTUMPDR1CA0ey156B6cOIEmSpI5zKqe6zrKXUNZqT8X99SRJkgaBZU9dZ9lLqwCmpQ4hSZKkjrPsqesse2k5qidJkjQYFpZ5sXvqEBoslr20Hpg6gCRJkrrG0T11lWUvLUf2JEmSBodlT11l2UvLkT1JkqTBYdlTV1n2Esla7e2AhalzSJIkqWv2KPPCxfnUNZa9dJzCKUmSNFimUK3GLnWFZS8dp3BKkiQNnr1SB9DgsOyl48ieJEnS4LHsqWsse+lY9iRJkgaPZU9dY9lLIGu15wC7pM4hSZKkrrPsqWsse2nsA4TUISRJktR1O5Z5sVnqEBoMlr00XJxFkiRpMAVgz9QhNBgse2l4vp4kSdLgciqnusKyl4Yje5IkSYPLsqeusOx1WdZqDwF7p84hSZKkZCx76grLXvftDsxKHUKSJEnJ+MW/usKy131O4ZQkSRpsW5Z5sUXqEGo+y173ufqSJEmSnMqpjrPsdZ+bqUuSJMmyp46z7HWfZU+SJEl7pA6g5rPsdd/OqQNIkiQpuZ1SB1DzWfa6KGu15wCejCtJkqQdUwdQ81n2usspnJIkSQLLnrrAstddTuGUJEkSwMwyL7ZMHULNZtnrLsueJEmSxmSpA6jZLHvd5TROSZIkjXEqpzrKstddjuxJkiRpTJY6gJrNstddfnsjSZKkMf5sqI6y7HXXDqkDSJIkqWdkqQOo2Sx7XZK12guAWalzSJIkqWc4sqeOsux1z/apA0iSJKmnWPbUUZa97rHsSZIkabzNyrxYkDqEmsuy1z2WPUmSJK0pSx1AzWXZ6x7LniRJktbkVE51jGWveyx7kiRJWpNlTx1j2eset12QJEnSmrZNHUDNZdnrnu1SB5AkSVLPWZg6gJrLstc9W6QOIEmSpJ5j2VPHWPa6IGu1h4E5qXNIkiSp51j21DGWve6YD4TUISRJktRzLHvqGMted8xPHUCSJEk9ybKnjrHsdYdlT5IkSRNZUOaFM8DUEZa97rDsSZIkaSLDwLzUIdRMlr3usOxJkiRpbZzKqY6w7HWHZU+SJElrY9lTR1j2usOyJ0mSpLWx7KkjLHvdYdmTJEnS2ixIHUDNZNnrDsueJEmS1saRPXWEZa87LHuSJElaG8ueOsKy1x2WPUmSJK2NZU8dYdnrDsueJEmS1sayp46w7HWHZU+SJElrMzd1ADWTZa87LHuSJElamxmpA6iZLHvdsXnqAJIkSepZM1MHmEgI4ZQQwhZruS+EEIbX4zWmhRDu0znq50+fjJxauympAzRd1mpPAWanziFJkqSe1RMjeyGEg4CjgeX1TVOB00MIY9enAf8dY/wRsCNwbAjhbcCrgc8DhwG/A2KM8Qv1c94L7BVCWDHBW24N5B05GAGWvW5wCqckSZLuT0+M7MUYrwSuDCH8J7APEOu7AvDrGONJACGE44CXAlsC2wD7A0+j2hz+TuCWEMKKGGMbmA5cWN++phM6eDjCstcNm6UOIEmSpJ7WE2VvnFkxxoPH3xBC+OC4q/8N3Ao8AzgJ2Al4I1X5+xzwQaqCCDAP+D3VaGEc9xoBOH3yo2s8y17nrXMusyRJ0sYYjZGhENb9QPW6npjGOc4vQghns/rI3q/G7owxxhDCfsC2wDuoft59J3AocA1wMfAm4M/AcIzxeyGEK9Z4jwB8E/hhB49j4Fn2JEmSNsFvli7lxL9fxbd33Y2bVqzg3667lhtWrGCzoSGOmb85B82Zs9rjl8fI2dddS7n0Hu4cHeWA2bM5ZYstCCHw52X38LbrruPu0VFmhCHevPVWbDt1GpfedhsX3HQjUwicvc027Dy9WtfifTfewNHz57PllKkpDl2TpydG9kIIc4FLWXXO3pr3HwU8nirvXOCrwMeBPwJnAh+IMV4dQvgOMHaO3j31QiyPjjHGNV5vKIQwLca4rCMHJMteF/h1myRJDXXbypW8+/rr2WHqNADOuf46nj5vHgfOnsM9o6Ocds01PGD6dLaauqqMff322xki8D877EAETrvmGr53113sP2sWrWuu4d2Lt2WbqVP57dKlnP7Pa7lw++354m23csmOGX9bvoxP3nILrS234rLbbyObNs2i1wzTUgeoReBnwKnA84GV9e3DMcbzQwjn1re9FPgUcBDwVmAXYA6wfwhhb6AE3gV8oX78u4A9Qwi7AVcDd1D9jByopoR+qBsHN4jceqHzLHuSJDVQjJHTr/0nr1i0iLGZlH9ZtowDZlWLcE8fGmLfWTP5xdK7V3veVlOmcPyCBYQQGAqBraZMYWWMlPfcwy7TprNNXQz3mDGDW0dXsmx0FIC7Rke5ecVKpoXAn+65h98sXcphc+d174DVSb0yADM28jYT2JNqhc3PAw9b4zHvAf5UXz8GuB54HvAi4JIY45IY4xdCCAVwa72wy2lUK3M+lGrVzm8Cj40xWvQ6yLInSZK0ET540008YtZs9p65agbejtOmcfkddwBw44oVXH77HVy1fPUZcQ+ZNYut60J3yS23cNXyZTxq9mz+sXwZO01bfYBn9+nT+evy5ZywYCGnXnM1/3PLzRw+dx4fvOkm7hgd5Zi//oVv33lHh49UXdArZW9skGKUajXOT9SXbPyDYow3148NMcZR4CVU5+xdCvxw3P57xwIfqqd/fg14JHAR1ZTPlcAZIQSHpjuoV/5iNZkje5IkNcz37ryTvy5fxpkLtl7t9tdusSVvv/46Lr75JhYMT+FJc+eybPXTlAC4e3SUt153LVtNmcK7Fm/LUAhMC0NMW2OxlWECy2Nkv1mz2G/WLGKMvOW6azl4zmx+cvfdnL/d9rzmmqs5cPac+7yH+kuZF1OKkXKivei6aQYwI8Z4K7Bk7MYQwqwQwg/qq2Pn180ENgshnEd1jt/rgW8DRwIfq7dneD/wN+CXwKIY4z3j3yyEsA/V1gwTniOoTWfZkyRJ2kCfuuUWrl2xnGOv+hsA/3fPPRz917/wikVb8NZtFt/7uPNuvIEH1IupjFkWI6+8+h//n707j7O7qu8//jr33tkSyLDvSLCiXpRN0Z8sQtW6xrUVtypatO4LyCK12n67Gltb0VbcLdaq7MoSUBBlUQSBBMJyLyAQkDUJSW4y+3K/vz/uxGyTyZ3J3Hvu8no+HvPIzGSSeY/BufO+53PO4Q3z5vH6jcYwD+zs5Nq+TVfplo0Ms3duw49rP1yzmpfvsCOrx8d5cc8cerNZ5mUc1GoROTYcahJFmqYrgA9M8v6BEMLRlVcrz16kaXofcMYkf835Ey8AD0/8OrSVz7d0u0NrSpa92nNlT5KkFnPWvvtu8vZfPvwwPzzgAL6+ciWl8XFeN28eT4+NcV1fHyftvMsmH3txaQ2HdfdsUvQA9uvooDA8xGOjI+zb0ck9Q0P0ZDLsPFH2bhsYYDhNOXruXG4ZGOC2wQGOS3dg7Xi5tl+s6qWDrZSiRjAxrqkmY9mrPcueJEkt7unxyoLMO3baib998gl+tGY1AGfssQedmQwrxsZYuPwp/mOffbm+r4/lY2PcPNAPVDYuvWHePN6+0878/Z57ceYTT5ABdshk+ce9KmOiI+UyV65by2f32BOAF/b0cM7qVbxl2UN8YJdd6/71qib8uVyzLqSTzJFr9sw/c1EeuCd2DklqFLsNrnnqBz//5z1j55BiGU1TckCY2J83nqaUgY5tXI6epukf/4xa0p75YmF57BBqLT6DUHt+V5YkYA5D/e/PXnH7Wzt/WR6g27KntrV5qcuGQHYrH7sxi17Lc0xSs86yV3t+Z5bUxtL0zzK3Lf107sK+fHjksBA4ppQNywfYO3YwSWo0I9v+EGl6LHuSpFl3QHjy0dNz5/3+1Zlb/6QjjB+28e+N50I2hbHgY5Akbcyyp1nnA23tubInqS3MYaj/pOyVt5+U+9ncnVl3WAjsN9nHBcgAfcBO9U0oSQ1teNsfIk2PZU+StB3S9OWZJUtPzV2w7uDw8OEhcMy2/kQGMuVAfza17EnShLF8seCpiZp1lr3ac2VPUst5Rnjq0dNy5z/w2szv5m8+prktgTSMZxjMjtcqnSQ1HUc4VROWvdqz7ElqCT0MD5yUvXLJSbkr5+4yxZjmtmRSMmNZhjote5K0nmVPNWHZkyRN6WWZJXecljt/3cHh4cOqGdPclgxkRnIMz/FHG0laz++IqgnLXu25siep6ewflj92Wu68379uBmOa25IlzQx3+IONJG3E74mqCcte7fl/XklNoYfhgfdmf77kA7kr5uzK2sNDYN9afJ6Qkh3qZKwWf7ckNSl/XlRNWPZqb23sAJI0leMzt995Wu6C0vPDQ4fOxpjmtmQgM9hFudafR5KaiGVPNWHZq71S7ACStLn9worHT8udd//rMjfP7wzjh9Tzc2cgM9AVUvCUcUmaYNlTTVj2amzZwgV9889cNA5kY2eR1N66GR58b/aqxX+dWzRnV9YeFgL7xMiRgdDfbdOTpI14obpqwrJXH2uBnWOHkNSejsvccedpufPXHBIempXTNGdDf3fsBJLUUFzZU01Y9urDsieprvYLKx4/NXf+/QsyNx/QGcbqOqZZjb5uMrEzSFIDseypJix79eG+PUk1183w4InZq5f8dW5R126Ujog1plmNdT2OtkvSRhzjVE1Y9urDsiepZl6aWXrXabnzVx8aHjw0BI6Onaca/T2u7EnSRjy9XTVh2asPy56kWbUPK584teOCe9+Q+e0BnWHs+bHzTFd/d+j0NE5J+qOnYwdQa7Ls1YdlT9J262Jk6D3Zqxd/MHd51+6VMc29Y2eaqb5uOmJnkKQGYtlTTVj26sOyJ2nGjs3ceddpufNXHxYeaJoxzW3p66YzdgZJaiCrYgdQa7Ls1YdlT9K07M3TT57acUHxDZkbD+hqwjHNbRnopit2BklqIK7sqSYse/XhpltJ29TFyNC7s79Y8sHc5Z17sObwENgrdqZa6e+iJ3YGSWogruypJix79eHKnqStOjpz192n585fdXj4/aEhcFTsPPUwmgu5FEYD7t2TJFzZU41Y9urDsidpE3vz9JOn5C68903ZG/frCqPPi52n3kLlKM4+YOfYWSSpAbiyp5qw7NWHZU8SnYwOvzv7i8Ufyl3WsQdrjmjlMc0qlMuBgWxq2ZMkXNlTjVj26sOyJ7Wxl2Tuvuf03Pkrjwj3H5ppkzHNKqTjWQazY7FjSFJ0KbA6dgi1JstefVj2pDazF6ueOiV3YfFN2d/s1x1GD46dp9EEKI9mGeq07ElSKV8sjMcOodZk2asPT+OU2kAno8Pvyl6z+MO5yzr2ZPURIXB87EwNLB3NMcxw7BiSFJ0jnKoZy159rIkdQFLt/L9wzz2nd5y/8gXhvkMc06xOgPJwB6Oxc0hSA/BwFtWMZa8Oli1cUJp/5qIBYE7sLJJmx56sWn5K7qLCm7O/3tcxzRlJhzpxiFOSXNlTDVn26ucR4LmxQ0iauQ7Gz+yLuQAAIABJREFURt6Z/eXij+Quze7Fqhc4pjlzAcYHOinHziFJDcCVPdWMZa9+LHtSk3pxKBRO7zh/xQvDvYdkAi+JnacVBEgHukK5cgidJLW1lbEDqHVZ9urnkdgBJFVvD1avODl30T1/nr1hn+4wmgfysTO1lJS0vzt2CElqCP6MqJqx7NXPw7EDSJpaB2Mj78j+avFHcpdk9nZMs6YCabmvhxA7hyQ1AH9GVM1Y9urHZ22kBvWiUCye3nHe8iPDfc/PhNQxzToIkK7rCVnHOCXJsqfasezVj2VPaiC7sWbFybmL7vmL7A379ISR5+Ke2rqqlD0fgyQJy55qyAfa+vH/yFJkOcZG3569dvFHc5eEfXjaMc240r4eOmKHkKTIBvPFwvLYIdS6LHv18yhQBjKxg0jt5oXh3uIZHectf1G493mZkP6/2HkEGSj3ddMVO4ckRebkl2rKslcnyxYuGJ1/5qIngX1iZ5Hawa6UVp6cu+jut2av39sxzcYTIO3vCZY9Se3OyS/VlGWvvh7GsifVTI6x0bdlr1380dylYV9WHuGYZuMKKWlfFz2xc0hSZJY91ZRlr74eAY6KHUJqNUeE++/9TMe5T704FB3TbBKBNB3oDnNi55CkyCx7qinLXn05ly3Nkl0prfxk7uJ7Tshet9ecMPIc4DmxM6l6AdKBLnaInUOSIrPsqaYse/Xl/6Gl7ZBjbPSE7HVLPpa7hIkxzeNiZ9LMBEjHsqEzhZEAnbHzSFIk/myomrLs1Zcre9IMHBZ+f99ncuc+8ZJM4XmZkL44dh5tvzBxm3oK/ZY9SW3MsqeasuzVl2VPqtIulJ7+ZO4nd78te92ec8Lwc4Bnx86k2RMqV9GQZuinzM6x80hSBGPAY7FDqLVZ9urLZ2+kKWQZH3tr9rrFH89eku4XVrzAMc3WlUkrK3tjGQaz5dhpJCmKx/LFwnjsEGptlr06WrZwwZr5Zy5aC8yLnUVqJIeGB+7/TO7cx4/K3HOwY5rtIZBWyl6Woa6x2GkkKQoXAVRzlr36+wPwvNghpNh2Zu2qT+Z+ctfbstfuMTcMPxc4KHYm1c/6PXsjOUbmDsdOI0lRPBA7gFqfZa/+fo9lT20qy/jYn2dvWPKJ7E/H9w/LHdNsY5mJX4c7GI0aRJLiuSt2ALU+y1793Qm8KXYIqZ4OCQ/ef0bu3MePztx9cDakL4qdR/GtX9kb6sQhTknt6u7YAdT6LHv1tzR2AKkedmbtqk/kfnrX27O/ckxTWwgTB7QMduHxLJLalSt7qjnLXv1Z9tSyMpTH/zx7w+JPZH8y/gzHNDWFzMQBLf1doTyxyCdJ7WR1vljw2gXVnGWv/u4HBoGe2EGk2fK88NDvP5M797FjMnflHdNUNTITDa+/O3YSSYrCEU7VhWWvzpYtXFCef+aiu4EjY2eRtsdOrFv98dwld74j+8vddwhDeeBZsTOpeWQmFvP6eghxk0hSFJY91YVlL46lWPbUhDKUx9+c+fXiT+Z+Mn5AeOoIxzQ1U+sPaFnXE7KOcUpqQ+7XU11Y9uJw356aysFh2QOfyZ376LGZO5/rmKZmQ2ai4K3r8XFIUluy7KkufJCN447YAaRt6aVvzcdylyx9V/aa3XYIQwcDfxI7k1rHRnv2OmNnkaQILHuqC8teHK7sqSFlKI+/KfObJZ/M/WR0fnjS0zRVM2FicnNdDx1xk0hS3S3PFwsrY4dQe7DsRbBs4YJV889c9Biwb+wsEkA+PPzAGblz/3BcZmk+G1L3k6rmslTu1+vvDp7HKanduKqnurHsxbMUy54imkdf6WO5S5e+K3vNLjuGwefhmKbqaP2evf5ur6GR1HYse6oby148S4HXxg6h9pKhPP7GzI1LPpm7ePTA8OQRIfDS2JnUntaPcfZ3MTduEkmqO69dUN1Y9uJx357q5rnhkQfPyJ37h+MzS5+TDWXHNBVdZuLXwS52iBpEkurPlT3VjWUvHsueamoefaWP5C5b+u7sL9aPaT4zdiZpvfWncY5nQ0cKwwG6YmeSpDpIcWVPdWTZi6cIjIDHjmv2BMrl12duWnJy7qLhZ4YnXuCYphpVZqOL1FPot+xJahP35YuFUuwQah+WvUiWLVwwNv/MRfcAh8fOoub3nPDIQ2fkznvkTzN3PDsbyi+MnUfalsyGrkc5w0CmzC7x0khS3dwUO4Dai2UvrqVY9jRDO9I/MaZ59c7zwuDzgQNjZ5KqlWXD0t54hsFcOWYaSaqb38YOoPZi2YvLfXualkC5vCBz8+0n5y4a+pPwuKdpqmll0g1Le2NZhrrGIoaRpPpxZU91ZdmL6/bYAdQcnh3+MDGmefuzc6H8gth5pO2V2ej1kRzDc4ejRZGkeunDkzhVZ5a9uG4GxvDfQZPYgYG1H85ddseJ2at3nhcGHNNUS9m47A13MBotiCTVzy35YmE8dgi1F0tGRMsWLuibf+aiJcCLYmdRYwiUy6/L/O72k3MXDT0rPOaYplrWxqdxDnbhDz+S2oH79VR3lr34rsOy1/YOCo8uOyN33sMvyyw5yDFNtYONT+Mc7MTjWSS1A/frqe4se/FdD5wWO4TqbwcG1n4wd/nS92av7u0N/YcA82Nnkuolu9Hr/d2hvNHhnJLUqix7qjvLXnw3AGU23cKilpWmr8387vZTchcOHhQeOzwEjo2dSIohm244jrO/O2YSSaqLB/LFworYIdR+LHuRLVu4YM38MxfdBRwaO4tq55nh8Yc/kzv3oVdkFh+UC+UjYueRYstAWP96X7dPdklqee7XUxSWvcZwHZa9ljOXwXUfzF1+x3uzV83bKfQfChwQO5PUKDZud+t6QsYxTkktzhFORWHZawzXA5+IHUKzIU1fk7nl9lNyFw48Ozx6hGOa0uSyG12qvq7HxyJJLc+VPUXhA2xjuD52AG2fA8Pjj5yRO++hV2Zu+xPHNKVt23hlr7+HzmhBJKn2BoClsUOoPVn2GsCyhQuWzz9z0b3Ac2JnUfXmMtj317lFt783+/N5O9F/SAg8I3YmqVlsXPb6ui17klrarfliYSx2CLUny17juA7LXhNI01dlbrvj07kL+p4T/uCYpjRDG49x9vUEz+OU1Mqc4FI0lr3GcT3wwdghNLkDw+OPnJ47/6FXZm77k44wfnjsPFKz22SMs5ueaEEkqfauih1A7cuy1ziuix1Am5rLYN/7s1fc/le5n++4E32HOqYpzZ7sRodvDnQxN14SSaqpdXg4iyKy7DWIZQsXPDr/zEXLgPmRo7S5NP2zzG1LP527sC8fHvHSc6lGsqR/vGdvoIsdYmaRpBq61v16ismy11iux7IXxfzwxB9Oz53/4Ksytz6zI4wfFjuP1Oo2HuMsZ0IuhaEA7t2T1Goc4VRUlr3Gch1wYuwQ7WIOQ/0nZa9cclLuyh13roxp7h87k9Qusilh47dT6LfsSWpBV8cOoPZm2WssntZUc2n6iszipZ/OXbju4PCwY5pSJBnSTd4uZxjIlNk1UhxJqoWH88XCvbFDqL1Z9hrIsoULfj//zEWPA/vEztJqDghPPnp67vwHXp255UDHNKX4Mput7I1nGMyVY6WRpJpwVU/RWfYaz3XAO2OHaAU9DA9MjGnO3YV1h4XAfrEzSarIbvb2aJahLo8wkNRaLHuKzrLXeBZh2dsuL88sXnpq7oK1B4eHDwuBY2LnkbSl7GZjnCMdjDAcKYwkzb4y8IvYISTLXuO5AhjDf5tpeUZ46tHTcuc/8NrM7+Z3hPFDY+eRNLXND2gZ7mA0VhZJqoHF+WJhVewQkoWiwSxbuGD1/DMX3QC8LHaWRtfD8MD7sj9b8v7clXN2Ze3hjmlKzSOz0T17AEOdjMfKIkk14JULagiWvcZ0CZa9rXpZZsnSU3MXrH1eWOaYptSksptOcTJg2ZPUWix7agiWvcZ0CXBW7BCNZL+w4vHTcufd/7rMzfM7HdOUml6GTcc4B7pDymb7+CSpSfUBv40dQgLLXkNatnDBsvlnLloKtHWp6WZ48H3Zny/5QO6Knl1Ze1gIXkkhtYrNxzj7vE5dUuu4Ll8sjMQOIYFlr5FdQpuWveMydyw9PXf+2ueHhw4NgaNj55E0+zYf4+zrIRMniSTNuitiB5DWs+w1rkuBz8cOUS/7hRWPn5o7//4FmZsP6AxjbVlypXaS3Wxlb11PyDrGKakFpMBPYoeQ1rPsNa7bgMeAfWMHqZVuhgffm71qyQdyV3TvRulwxzSl9rH51Qvrenw8ktQSbswXC0/EDiGt54Nrg1q2cEE6/8xFlwIfiZ1ltr00s/TO03PnrTkkPHSYY5pSe9r8gJb+HjpjZZGkWXRR7ADSxix7je0SWqTs7cuKJ07tuOC+12duekZnGDskdh5JcW0+xtnXbdmT1BIujh1A2phlr7H9ClgLzIsdZCa6GBk6MXv14g/mLl8/prl37EySGkNmszHOvu7geZySmt2t+WLh4dghpI1Z9hrYsoULRuafuejnwAmxs0zHsZk77zotd/7qw8IDnqYpaVLZzcc4u+mJlUWSZokjnGo4lr3GdwlNUPb2YeXEmOZvn9EVxp4fO4+kxpZJNx3jHOhibqwskjRLLHtqOJa9xncFMEYD/lt1MTL07uwvFn8od1nX7pSOcExTUrWym7092MUOUYJI0uy4M18s3B87hLS5hisQ2tSyhQtWzz9z0fXAy2NnWe+YzF13n5Y7f9Xh4feOaUqakWy66SXq5UzIpjAUwL17kpqRq3pqSJa95nAJkcve3jz95KdzF9z7xuyN+3eFsefFzCKp+WU2O40TIIU+y56kJmXZU0Oy7DWHS4Gv1PuTdjI6PDGm2bEHa44Igb3qnUFSa9r8nj2AcoaBTDlGGknaLvfli4W7YoeQJmPZawLLFi5YNv/MRbcDh9fj8x2Vufvu03PnrTo8/P6QTOCoenxOSe0lm25Z9sYzDOYse5Kaj6t6aliWvebxf9Sw7O3FqqdOyV1QeHP2xv27wqhjmpJqarIxztEcw11jMdJI0nax7KlhWfaax4+AL7LlIXYz1sno8Luy1yz+cO6yjj1ZfUQI/Ols/d2SNJXJxjhHcozEyCJJ22FZvli4LXYIaWsse01i2cIFT8w/c9E1wKu29+96Sebue07Pnb/yiHD/oY5pSoohm265sjfUwWiMLJK0Hc6NHUCaimWvufyAGZa9PVm1/JTchfe8Ofub/bvD6MGznEuSpiU7yZTCUCfjMbJI0nb4n9gBpKlY9prLxcDXobrLhzsZHX5n9pdLPpy7NLsXq1/gmKakRpGZ5ICWwa4wDmmMOJI0Ezfmi4X7YoeQpmLZayLLFi4YmH/moouBE6f6uBeHQuH0jvNWvDDcd0gm8JI6xZOkqk22Z6+/26YnqamcEzuAtC2WvebzAyYpe3uwesXJuYvu+fPsDft0h9E8kK9/NEmqzmSncfZ5nbqk5jEAnBc7hLQtlr3m80vgMWDfDsZG3pn95eKP5C7N7sWqI0Lg+NjhJKka2ZTM5u/r69nyfZLUoC7OFwtrY4eQtsWy12SWLVxQfsdn//3rn85d8Mojw72HOqYpqRlNNsa5ridk3bMnqUmcEzuAVA3LXhM6t/OfLwb+OXYOSZqp7ORjnB0xskjSND1MZdJKaniOzDSjpFQAboodQ5JmKpNuefVCX49PQEpqCt/PFwuOIagpWPaa1/diB5CkmZpsjLOvh64YWSRpGlIc4VQTsew1r3OpnAQlSU0nQ7rlAS3dwfM4JTW66/PFwkOxQ0jVsuw1q6S0DrgwdgxJmonJLlXv72ZOjCySNA3/EzuANB2Wveb2ndgBJGkmMpM8/gx0WfYkNbQ+fKJdTcay18yS0g3AXbFjSNJ0TTbGOdTJDjGySFKVLsgXC/2xQ0jTYdlrfmfHDiBJ05WdZIyznAnZFAZj5JGkKjjCqaZj2Wt+PwDWxg4hSdORYcurFwDSQF+9s0hSFe7IFws3xA4hTZdlr9klpT7g+7FjSNJ0hEnGOAHKwVOGJTWkr8YOIM2EZa81fI3KvS+S1BQmO40TYCzLUL2zSNI2rAR+FDuENBOWvVaQlO4FrokdQ5KqNdlpnACjlj1Jjeeb+WLB701qSpa91vHfsQNIUrW2VvZGcozUO4skTWEUD8NTE7PstY7LgYdjh5Ckakx29QLAcCdj9c4iSVO4MF8sPB47hDRTlr1WkZTGgW/EjiFJ1Qjp5I8/Q5Y9SY3lrNgBpO1h2Wst3wGGY4eQpG3JbuXxZ6ArlOudRZK24qZ8sfC72CGk7WHZayVJaSVwfuwYkrQtW7tnr7/Lk4UlNYyvxA4gbS/LXuv5cuwAkrQtk967APT11DWGJG3NY8CFsUNI28uy12qS0hLg6tgxJGmb0nSLkc2+Hh+XJDWEs/PFgnuI1fR8UG1NX4wdQJKqsEXZW9cTJh3vlKQ6GgK+FTuENBsse60oKV0D3BY7hiRtw5Yre910xAgiSRv5Yb5YWBk7hDQbLHuty9U9SY1usjFOy56k2DyYRS3Dste6LgLujx1CkqYwvvk7+rpDV4wgkjTh5/li4c7YIaTZYtlrVUmpDHwpdgxJmsJkK3vdMYJI0oR/iB1Amk2Wvdb2feDJ2CEkaSu2uFOvvwsvX5AUyzX5YuG3sUNIs8my18qS0jBwVuwYkjSZMMnK3kAXc2NkkSRc1VMLsuy1vm8Aa2OHkKRJbFH2hrrYIUYQSW3vunyxcEPsENJss+y1uqRUolL4JKnRbFH20hAyKQzECCOprf1j7ABSLVj22sOXqVwQKkkNI0yyZw8gDfTXO4uktvbrfLHwy9ghpFqw7LWDpPQk8PXYMSRpM1tcvQBQDq7sSaqrf4odQKoVy177+AL4bLmkxrG1lb2xLIP1ziKpbd2ULxauih1CqhXLXrtISiuA/4odQ5I2ssWePYDRLMP1DiKpbbmqp5Zm2Wsv/44nc0pqHJOu7I10MFLvIJLa0q35YuGK2CGkWrLstZOktIrKYS2SFN1k9+wBDHcwVu8sktqSq3pqeZa99vNlYFXsEJK0tT17g52WPUk1d3u+WLg0dgip1ix77aZy796XYseQJLaysjfQFSZ9vyTNIlf11BYse+3pq8CK2CEktbeQTl72+rsnX/GTpFmyGPhJ7BBSPVj22lFS6ge+GDuGpPYWSCctdX3dhHpnkdRWTssXCz6ppLZg2WtfZwNPxA4hqa1NXvZ6LHuSaubyfLHwq9ghpHqx7LWrpDQI/HPsGJLa19YOaFnXE7L1ziKpLYwDZ8QOIdWTZa+9fQsoxg4hqT1t7eqFvh466p1FUlv4Tr5YKMQOIdWTZa+dJaUx4PTYMdSeRsdTymnK8NjsbZsYK6eMl6v/O8uTbxlTnWxtZa+v27Inadb1AX8fO4RUb5a9dpeULgeuiR1D7eeSe8c4/pwBXvyd/j++74FVZRb8aIDjz+nnLecNcMtj41v982ma8tbzB/jKTcN/fN/vHhvn+HMGeMZZfZt87Fk3DXP4N/o49nv9LO8v//HPn/yzYQZGLXyxbLXs9YSuemeR1PL+LV8sPBU7hFRvlj0BnMpWxqmkWnnrwR3c8FdzmdOx4SyOD10+yBde0cV175vLOW/qIblumP6RycvY2beMsmYopTu34c8fvX+OX580l2fuvOm3tp8/MMaSD83l88d18eM7RwH4ys0j/EU+t8nnV31tdYyzm556Z5HU0h4D/iN2CCkGy54gKd0BnBM7htpTdqOu1T8Kh+5ZOZujtzvw7F0yFFdu2QduenSM3/xhjPcc2kF2ku9i2c3621gZhsZg5UBKVy5w3bIxAI6fn5u1r0PTF9LJV/YGLHuSZtfn88XCQOwQUgyWPa33OSrz7FI0O3TCzY9WitiDq8tc/eAYD67etOyt6C/zuV8O843X91R98/ZHj+zkjecO8IuHxnjZ/Cz/t3SUWx8vc+z3+ln8xNZHRVVbW7tnb6CLufXOIqllLQW+HzuEFItPa6siKT1B0vtvwD/GjqL29a3X93D61UOsHBhm/k4Z3n1oBz0bHdUxVk75wGVDnPWabuZ1VT9++ZZ8B2/JdzA6nvLhy4c4av8sfSMp//bKLj5x5RAXvW1ODb4abcvW9uwNdTI3hTTgfXuStttp+WLBrSpqW5Y9bexLwAeB/WIHUXs6cOcMF25UvD5w6SALDtrwbeqWx8YprCjz8SuGAHiyL6WcwhX3j3Hx27dd2JJrhznlqE4uu3eM1x2UY58dM3R6o1s0Wyt7aQgZoB9c4ZO0XX6eLxaujh1Cismypw2S0iBJ72eB/40dRe0hTTfdtPWBSwc56YgOjt4/x31Pj/Pg6jLP32PDtPlR++e47xM7/PHt/1kywlgZ/vqFnZv+vZN8rvPuGuV5e2R5/h5Zbnt8nDuXj/OsXTL0j8zyF6Wqba3sAaSB/pBa9iTNWBmvl5Lcs6ct/B9wa+wQag+DY2xy2ubfHNvF5381zEv/p5+PLhri7AXdhBC4/clxTv7Z0BZ/vn+USa9OWDmw6fuW95e5/clx3nVIZSb0Lw7u4Fu3jfL/vtPPJ17cucWfV31kpih74wEPU5C0Pc7JFwt3xg4hxRZSLxXW5pLeY4AbcL+MWkiapoTgf9KN5GX773vrylz2yMl+73+/NFboHiVf70ySWsJq4Ln5YmF57CBSbK7saUtJ6Td4FYNajEWv8WSmOE91NMdwHaNIai1/Y9GTKix72pozgFWxQ0hqXVPt2RvO4W5KSTPxW+BbsUNIjcKyp8klpZXAZ2LHkNS6pix7HYzVM4ukljAGfDhfLLhHSZpg2dNUvgvcGDuEpNaUmeLHscEuvO1e0nR9JV8sLI0dQmoklj1tXVJKgQ+Dz7BLmn1TrewNdAUvQZY0HX8A/j52CKnRWPY0taR0J/CV2DEktZ4MWz8Our97itNbJGlLn8wXC/2xQ0iNxrKnaiTAo7FDSGotU92z19ft1S+SqnZpvlj4aewQUiOy7GnbklIf8KnYMSS1lpBOUfZ6fHySVJV+4BOxQ0iNygdTVScpXQxcETuGpNYx1QNQX3fI1i2IpGb2D/li4ZHYIaRGZdnTdHwcGIwdQlJrmGrP3roecvXMIqkp3Ql8OXYIqZFZ9lS9pPQQ8PnYMSS1hqk25fX10Fm3IJKaUUrlTj1PDJemYNnTdH0Z796TNAsyU+7ZC131zCKp6Xw3Xyz484i0DZY9TU9SKgN/BQzFjiKpuU29Z4/uugWR1GyeAM6IHUJqBpY9TV9Sug/4XOwYkprbVHv2BrqYU88skprK+/PFwurYIaRmYNnTTDnOKWm7ZKa4Nn2gm7n1SyKpiXwrXyxcGTuE1Cwse5qZDeOcns4paUamulR9qIO56RS/L6ktPQB8OnYIqZlY9jRzjnNK2g6ZqbpcCIHKZcmSBFAG3psvFvy+IE2DZU/b6ywc55Q0A1ONcQKkwbIn6Y++lC8WfhM7hNRsLHvaPo5zSpqhbT0AjWcYqEsQSY3uTrznV5oRy562n+OckmZgqj17AGMZr3iRxAjwnnyxMBI7iNSMLHuaLWcB18UOIal5ZLZ+8wIAozmG6xRFUuNK8sXCHbFDSM3KsqfZURnnfDewKnYUSc0hu42VvZEco/XKIqkh/Rb4t9ghpGZm2dPsSUqPAh+IHUNSc8hAmOr3hzste1Ib6wdOzBcL47GDSM3MsqfZlZR+AnwzdgxJjW9bY5yDnfhDntS+zsgXC7+PHUJqdpY91cIpwD2xQ0hqbNlt/H5/VyjXJYikRnNVvlg4O3YIqRVY9jT7ktIg8E7wJD1JW7etB6D+7qn39K1X3sYKoaSmsoLKlU6SZkFIfZBUrSS9Hwf+K3YMSfX3m0fG+Ifrhhkcgz3nBr75+m52nbOh3q0eTHnBZWMrlo+H3QF2f/3u7HjojgCsuWkNq361ip2eGl/74nLXvGTPvVhbLvOpxx5jIC3z5729vGfnXQC4c3CQu4eHeMdOO0f4KiXNsjLwmnyxcHXsIFKrcGVPtZOU/hu4LHYMSfW1bjjlUz8b4vwT5nDDX83lfYd3cNrVm96icOpVQ7zk1Ts98szPPpMDPn0A5ZHKxObQo0Osu2MdB37mQD76lmfe8abeXp4eH+eKtWv5yG67cvEB8/lNfz8Aa8bHubBU4oTener+NUqqiX+y6Emzy7KnWjsJeDx2CEn1c9UDY7zh2R3s1F05bHPBQTkWP7HhrJW1wynjKdy3uG+3B7/wII99+zF6ntEDwJob1zDvhfN45CuP8F+XPXTYvUPD7JXL0RkCq8fHGU5TRtOU8TTlyyuWc/Juu5ENUx7qKak5XA38Y+wQUqux7Km2ktJK4EQqoxmS2sCyNWWes9uGh5cQArvPCawbrmwbeHhNmaseGGPPfTr7DzzzQHZ/4+48+p1HARh5aoTSTSX2++B+/Plfzr/3rqEhru/v5w2987ipf4CPPfYo79hpZ7696mn27ujgI489yheWP4VbEqSm9jjwl/liwZ8VpFlm2VPtJaVrgH+NHUNSfXTlAl2bHbWZzcDoxI9xQ2NwQG+GI4/ZcUUIgZ4Desj2ZBnvH6c8Wmbn43YmOzfLwA6ZjhN32ZkbB/qZm8nyL3vvzXf3fwadIbBLNsfiwUG+ud/+ANw55HlQUpMaA96eLxZWxA4itSLLnurl74GrYoeQVHvP3S3DvU9v+gT94+tSdu6uvP7MnQO5DGQ2WoxLx1PSNKVz905CtjKW2dcdusZTKG/0cX8YGeG3A/2c0NvLvEyG3myWF/fM4Q+j3r8uNanP5ouFX8cOIbUqy57qIymVgXcBD8eOIqm2Xrh3lp8UR1k1WGlpl983ylH7ZQkTe+t26Ql05+ChwsA8gOEnhknHUnI75Jh35Dye/sXTpGMpfV1pz8WlEkfPnQPAULnM159+mk/ttjshBNaWy4ymKfcND7NHLhfpq5W0HS4FvhQ7hNTKvHpB9ZX0vhD4NdAdO4qk2rnmwTH+9pfDzOmAPXcIfPN6kpNZAAAc6klEQVT1PZx9ywiH75XlNc/Kcd/T47z80tE1q7tyO4VcYO/37E33PpVvC8svW87a29aSHWPsfYM75D66666EEPjW00/z6h135IDOTgAuKZX49qqnOaCzk//aZ18yHtQiNZNlwAvyxcLq2EGkVmbZU/0lvScB340dQ1JcX9hl5+t/1LvjcVv7/e6RtP9//2N87vq30zT94+qgpKY2DBybLxZujR1EanWOcar+ktL3gG/HjiEprhxTP9k41MGclA0fZNGTWsanLXpSfVj2FMsngFtih5AUTyZl6vZWaXd99UkjqU7OzRcLZ8cOIbULy57iSErDwFuBlbGjSIoju42VPYByoL8OUSTVx73AX8cOIbUTy57iSUqPAO8AxmNHkVR/1TwAlTMM1jyIpHpYDbwxXyy4Wi/VkWVPcVUuXP9c7BiS6i+7rTFOYCxr2ZNawBhwQr5YuC92EKndWPYUX1JaCPw4dgxJ9ZWpYoxzNMtwHaJIqq2P54uFa2KHkNqRZU+N4iTgt7FDSKqfbR7QAgx3MFqPLJJq5qx8sfDN2CGkdmXZU2NISkPAm4GHY0eRVB+5Kj7Gsic1tSuAU2OHkNqZZU+NIyktB14PrI0dRVLtZUi3ubI32OUBTlKTugt4R75YKMcOIrUzy54aS1K6C3g7ntAptbxqDmgZ6Arb3tgnqdEsB96QLxbWxQ4itTvLnhpPUvoZcErsGJJqq5oDWvq6q/ggSY1kGHhLvlhYFjuIJMueGlVS+i/ga7FjSKqdalb2+rq3/TGSGsr788XCjbFDSKqw7KmRfQr4WewQkmojQxVlr8fHKamJ/Eu+WPhh7BCSNvBBVI0rKY1T2b93d+wokmZftooDWvp6QjWHdkqK70Lg87FDSNqUZU+NLSmtpXJC55Oxo0iaXdkqduP19VR1Q4OkuG4FTswXC+6xlRqMZU+NLyktA14DlCInkTSLslWMca7rprMeWSTN2INUTt4cjB1E0pYse2oOSekO4I3AUOwokmZHJq1qjLOrHlkkzchTwKvyxYLTN1KDsuypeSSl64F34B18Ukuo5oCW/m566pFF0rStBV6TLxYeiB1E0tZZ9tRcktIlwIdix5C0/aoZ4+zvZk49skialmHgTfli4fbYQSRNzbKn5pOUvgt8NnYMSdunmjHOwU7m1iOLpKqNA+/MFwvXxg4iadsse2pOSekLwJdjx5A0c9WMcQ53hjkplOuRR1JVPpovFn4SO4Sk6lj21MxOBf4vdghJM5OrYmVvQl9Ng0iq1pn5YuFbsUNIqp5lT80rKaXAXwFXxo4iafoyVT4GlQP9tc4iaZu+kC8Wvhg7hKTpseypuSWlMeCtwI2xo0ianmoOaAEoZ/D+Limu/84XC+6Vl5qQZU/NLykNAK8Ffhc7iqTqVXNAC8BY1vs1pYj+F/hk7BCSZsayp9aQlNYCrwJujR1FUnWqOaAFYCTHcK2zSJrUxcBJ+WIhjR1E0sxY9tQ6klKJSuFbHDuKpG3LpFWXvdFaZ5G0hZ9TuWJhPHYQSTNn2VNrSUqrgVcCd8SOImlqWaob4xzqtOxJdXYl8OZ8sTASO4ik7WPZU+tJSquAPwPujB1F0tZVe0DLYCeuLEj1cymVoudeWakFWPbUmpLSSuAVwN2xo0iaXCat7jFooCt4qbpUHxcDb3VFT2odlj21rqS0Ang5UIgdRdKWMqRVPQb19dQ6iSTgfODt+WLBsWmphVj21NqS0nIqhe/e2FEkbara0zj7uqv7OEkz9kPgXfliYSx2EEmzy7Kn1peUnsTCJzWcbJWncfZ3k611FqmNnQOc6KmbUmuy7Kk9JKXHgZcCS2JHkVRR7Rjnup7gY5VUG9+mco+e+2KlFuUDqNpHZQ/fy4DfxI4iaRpjnD101DqL1IbOBj7khelSa7Psqb1suHj9qthRpHZX7Wmc63rorHUWqc2clS8WPmbRk1qfZU/tJykNAG8ALoodRWpn1V6q3tcTumudRWoj/54vFk6JHUJSfVj21J6S0gjwdiob0yVFkEmrO3ilvxvLnjQ7/iVfLJwRO4Sk+rHsqX0lpXHgJOCrsaNI7ajaPXv9XcypdRapxZWBT+WLhc/FDiKpvnKxA0hRJaUU+BRJbwn4fOw4UjvJVPmE42AXO9Q6i9TChoB354sFty5IbciVPQkgKf0dcGrsGFI7yVS5Z2+kI/Sk4B1g0vStBl5p0ZPal2VPWi8p/SfwbmAkdhSpHVR7GueEvpoFkVrTI8Ax+WLh17GDSIrHsidtLCn9EHg1sCZ2FKnVVTvGCVAODNQyi9Ri7gBeki8WCrGDSIrLsidtLildCxwNLIsbRGpt1Y5xAoxnLHtSlX4BvDRfLDwRO4ik+Cx70mSSUgF4CXBr7ChSq6r26gWAsSxDtcwitYj/A16XLxbWxQ4iqTFY9qStSUpPAX8KXBY5idSSpjPGOZpzL620DQuBE/PFwmjsIJIah2VPmkpS6gfeAnwtdhSp1Uyn7I1Y9qStKQMfyxcLf5MvFtLYYSQ1FsuetC1JaZyk9HHgNMAHUmmWZNK06segoU7GaplFalKDwF/ki4WzYweR1Jgse1K1ktJ/AG8D9w5JsyFM4zFooMt79qTNPAG8PF8s/DR2EEmNy7InTUdSupDKPr7HIyeRmt50xjgHukK5llmkJnMT8MJ8sXBT7CCSGptlT5qupHQz8ELgt7GjSM0sO43HoP7uWiaRmsp3geO9WkFSNSx70kwkpSeprPB9J3ISqbmlaVX7YNf1UPWdfFKLGqVyEMsH8sWCBxZJqoplT5qppDRCUvpr4KNUHoQlTV9V45n93dXfySe1oKeAV3gQi6TpsuxJ2yspfR14OZUHY0nTU1XZW9cTLHtqV7cAR+aLhRtiB5HUfCx70mxISr8GjgRujR1FajJVlb2+HjpqHURqQN8HjssXC4/GDiKpOVn2pNmSlB4FXgr8IHYUqYlUubJHZ62DSA1kDPhUvlh4X75Y8LofSTNm2ZNmU1IaIimdCJwCXgItVaG6lb3u0FXrIFKDWAm8Ml8sfDV2EEnNz7In1UJSOgt4GeDojTS1qi5L7++mp9ZBpAawhMr+vGtjB5HUGix7Uq1U9vEdDlwRO4rUwKq6emGgizm1DiJF9m3gmHyx8HDsIJJah2VPqqWk9DTweuB0vJ5B2kKocoxzsIu5tc4iRbIGOCFfLHwwXywMxg4jqbVY9qRaS0opSelLwHGAz9hKm6qq7I10hJ60ypFPqYn8BjgsXyxcGDuIpNZk2ZPqJSndBBwB/DR2FKmBVDXGOaGvZimk+ioD/wQcny8WHokdRlLrysUOILWVpLQaeAtJ7yeBfwePk1d7C1Cutu2VA/3ZlN6aBpJq7zHgL/PFwnWxg0hqfa7sSTEkpa8CxwAPxo4iRVbVGCfAeAb3M6nZXUJlbNOiJ6kuLHtSLEnpVipjnT+OHUWKpdoDWgDGsni5tJrVEPDxfLHw5nyx8HTsMJLah2VPiikprSUpvQt4F7A6dhwpgqr37I3mGKllEKlG7gFenC8WvhY7iKT2Y9mTGkFS+jFwKPCL2FGkeprOyt5wh2VPTefbwIvyxcKdsYNIak+WPalRJKVHgVcBJ4PjamobVa/sDXUyVssg0ixaAbx14u68gdhhJLUvy57USCp38n2Fyl6+W2LHkWptOit7g53es6em8GPg4HyxcFHsIJJk2ZMaUVIqAkcBnwNH19TSql7Z6+8OVRdDKYLHgTfli4V35YuFlbHDSBJY9qTGlZTGSUr/ArwYuCN2HKkWQlr9yl5/dy2TSNvlf4Dn5YuFS2MHkaSNWfakRpeU7gBeBPwTMBo5jTSrAmnVZa+vm1DLLNIMPAK8Ol8snJQvFtbEDiNJm7PsSc0gKY2SlP6Oyl6+G2PHkWZLmMYYZ18P2VpmkaYhBb4OPD9fLFwVO4wkbY1lT2omSelu4FjgI0Apchppu02n7K3rCZY9NYIHgJfli4WP5ouFdbHDSNJULHtSs6mc2PkNIA9cGDuOtJ2ms7LXUcsg0jaUgf8EDs0XC9fFDiNJ1cjFDiBphpLSE8AJJL1vAL4G7B85kTRt07l6YV03nbXMIk2hAJyULxZuih1EkqbDlT2p2SWly4CDga8wjR+cpUYwvT17wfM4VW/9wGeBwy16kpqRK3tSK0hKfcDJJL0/BL4FHB45kVSV6ZS9/m56aplF2syPgdPzxcJjsYNI0ky5sie1kqR0C5VrGk4GPAZcDW869+wNdjGnllmkCUuB4ycuR7foSWpqlj2p1SSlMZLSV4CDgG/iaKcaWKb6hT0GuphbwyjSauDjwAvyxcL1scNI0myw7EmtKimtJCl9GHgB4MlxakjTGeMczYXuFMZqmUdtqUzlibGD8sXC1/LFwnjsQJI0W9yzJ7W6pHQH8KckvScA/w4cEDmR9EfTKXsT+oCdapFFbelG4BP5YmFx7CCSVAuu7EntIildADwX+DtgIHIaCZh+2SsH+muVRW3lSeBE4FiLnqRWZtmT2klSGiIp/RPwHConzUlRhXR6ZW88y2CtsqgtjAJfAp6dLxZ+kC8WpruyLElNxbIntaOk9ChJ6V3AsVTGmKQopruyN5plqFZZ1NLKwI+AfL5YOD1fLKyLHUiS6sE9e1I7S0q/AY4h6X0D8C/AIZETqc1kSKdb9kZqlUUt6zLgb/PFwp2xg0hSvbmyJwmS0mVULmJ/D/BQ5DRqI2GaV4MMd1j2VLXrgGPyxcIbLXqS2pVlT1JFUiqTlP6Pyn6+j1M5wECqqek+CA11evWCtuk24NX5YuFP88WCY+qS2pplT9KmktIoSelrwLOAvwVKkROphU33gJbBrumtBKqtFIETgBfli4WrYoeRpEZg2ZM0uaTUT1L6V+BA4N/AUxA1+6a7Z6+/K1j2tLlHgPcDz88XCxd6wqYkbWDZa2AhhL8KIey00dtdIYQ3hRA+vtH7QgghW8Xf1RlC2OLfe+LPd81earWcpLSapPQZKit9Z+EdfZpFYZof399dkxhqTiuAk6lco/C9fLEwHjuQJDUaT+NsUCGEALwOuDmE8FVgDDga+Hfggo0+9ADgfSGEhcCngZ8CbwDuBdI0TS+Z+LivAs8PIUy232UvKpdtS1uXlB4HTiHp/VfgFOBjwLy4odTsMtO8eqGvxycpxZNUnnj6Wr5Y6IsdRpIamWWvcR0LXA3sBvwyTdNzQgjfAX6QpukIVFb+qPzAvQewN3AU8BZgF6AfWBNCGEvTdBHQBXx74v2be3+tvxi1kKS0AvgsSe+/AZ8APgXsGjeUmtV09+z1dYfp9kO1jt9TecLz+/liYTh2GElqBpa9BjQxlvmPwD+w6bHka4BeKqMrAOdQOTzjbcBHqeyt+hyV8vcT4LtsmJLqBe4HRtn0J6UAJLP/VajlJaU1wD+R9P4n8GHgVCpPOkhVy06zua3rYZtj62o5twFfBC7KFwvu2ZSkabDsNabPArdv9PaZIYT3USlzx4YQRoB/SNP0mhDCkcC+wH8AWeA/qYx/PgH8L/B5KvemZdM0vTGEcO1mnysAvwJurt2Xo5aWlPqB/yDp/W8qq8RnUBkvlrYpM81Vur4eOmoURY3nGmBhvlj4RewgktSsLHuN6QfAn2709sKJMc63A33Az6hs69udyp6pq4AfAw9QWRH8epqmj4cQfg1/vJNqeOIglpel6aan34UQMiGEzvXjodKMJKVh4GyS3m8DfwmcDhwcN5QaXZjmROa6HjxQqrWVgYuAL+aLhdtih5GkZmfZa0Bpmi6rnM+yhVuBEyf24BFC+BhwPnAc8K/AnwA7AEeFEA4BCsCXgUuA8YnXnxdCOAh4nEpxDBMv5wDfq91XpbaRlEap/Pd0Dknvq6js6Xst0z94UW1guhvw+nuC53G2pmHg+8CX8sXC/bHDSFKrsOw1iRBCL5WC9uyJt48EvgLMpVL23gFcQWXv3grg1DRNPzbxsXmglKbpR0MIL6GyavhFKsXwVOALaZq62V2zLyldBVxF0vts4JPAe6n8dycBMxjj7KKnRlEUx1rgG8CX88XCk7HDSFKr8QjrxrV+xW396y8GFgJLJgrb29I0Xb3+49I0LQMfobJn70oqVzasP8jgfcD3QggnUDnh82jgf6iMfI4D/xBCcB+Maicp3UdS+jiwH3AasCxuIDWK6a7sDXYxp1ZZVFe3Ax8C9s0XC5+x6ElSbbiy17i6gE5gTyr7ny6kctDKrcDfAd+c+LgeYMcQwjeonLT5WeAG4K3AjyauZzgbeARYCuy2+SpeCOHQic83WuOvSe0uKZWoHOZyFvBGKhciHxc3lGKa7p69gS7m1iaJ6mAIOA/4Rr5YuCl2GElqB2Gzszokqb6S3sOp3Nf3dvAH+Xbznr33vP727q5pFf7zvjA2GvBUziZyH5UnKM/JFwurYoeRpHbiyp6kuJLS7cD7SXpPAd4JfAA4Mm4o1ct09+xN6AN2nuUoml1jVA4H+zrwy3yx4DPLkhSBZU9SY0hKa6k8+/9Nkt7DqJS+v8Qf6ltaZgYVoJyhP1v2v4sG9SjwbeA7+WLh8dhhJKndWfYkNZ6kdAfwCZLe04G/oFL8jsfrG1rOdA9oARjLMJgt1yKNZqhM5fCvrwOX54uF8ch5JEkTLHuSGldSGgJ+CPyQpPdZwPupXN+wd9RcmjUzGeMcyzLcNVaDMJqu24AfAee6iidJjcmyJ6k5JKXfA39D0vt54JVU9ve9Gdgxai5tl5mMcY7mGMabQWN5gErB+1G+WCjGDiNJmpplT1JzSUpjVO6SvJKktwdYQKX4vQ7ojhlN0zeTy16HO7wmps6WU7ky4Yf5YuHm2GEkSdWz7ElqXklpkModlBeS9M4D3kKl+L0Cv781hSzTv/9nqBOHOGuvD/gplTHqX+SLBf83l6Qm5A9DklpD5TTP7wPfJ+ndHTiBSvE7Bg92aViZdPr/NgOdeABIbYwCP6dS8C7NFwsDkfNIkraTZU9S60lKK4CzgbNJevcH3jTxcjxext1QsjP4M/3dIZ3BIZ6a3FrgZ8BlwBVeei5JrcWyJ6m1JaU/AP8N/DdJby/wGirF77XATjGjaWZ79vrdmbm9HqRS7i4Drs8XC+6BlKQWZdmT1D6SUonKQRPnkfTmgOOAN068HBgzWrvKTH/LHn3dM+qI7awM/JaJgpcvFu6JnEeSVCeWPUntqXKq5y8nXk4m6T2ESul7A3AkM5sw1DTNpLX19YSZ3MXebtYBV1EpeIvyxcLKyHkkSRFY9iQJICndCdwJ/AtJ707Ay6ic6vlnwHNiRmtl2Rl0tnU9PnZtxT3ANcDlwLX5YmEkch5JUmQ+YErS5pLSGuAnEy+Q9O5HpfS9YuJl72jZWsxMrl7o6/GQHSpLm/cA1wLXAdfli4XlURNJkhqOZU+StiUpPQqcM/ECSe/z2LDqdzwwL1KyppeZwbUY63rorEWWBrdxubuWSrlbETOQJKnxWfYkabqS0t3A3cBXSXozwCHAUcDRE78+K2K6ppKdwQEt/d2hHc7jTKn8N3YtG8qd++4kSdNi2ZOk7ZGUysAdEy/fqLyvd3c2LX8vAnoiJWxoM7x6Yc6sB4lvDbAYuA24icqVCJY7SdJ2sexJ0myrXOp+6cQLE9c8HE6l+B0FHAEchCd+kkmnP8Y50NX0xXkVG4rdbcBt+WLhwbiRJEmtyLInSbVWuebh1omX/6q8r7cHeD5wGJUieBhwKG22/y87gysUBrvYoQZRauVpNip1wOJ8sfBQ3EiSpHZh2ZOkGJLSIHDLxMvE+3oDMJ9NC+BhE++b9gpYM5jJGOdYNnSmMBJoqINa+oH7gfuAe4HbqazYPRw1lSSprVn2JKlRJKUUeGji5acb3t/bTeXQl4MmXp690etNfQ3ETO7ZA0ihL8Aus5tmm0ap/Nvct/lLvlh4rM5ZJEnaJsueJDW6pDQE3DXxstnv9e7AliXwQGDfiZeuuuWcgSzpjFYsyxkGMuWalL1B4AlgGVuWuofyxcJYDT6nJEk1YdmTpGaWlPqAJRMvk/x+725USt9+bCiAm7++Uz2iTmYmB7QAjGcYzJWn9UcGgMepFLn1vz6x+fvyxcKameSRJKkRWfYkqZUlpZXASipXQ2zlY3p7qIxE7rzZr5O9b/2vc4HuiZcuZrincJrHkY4CfUDfQBf3d42xEiht5WUVGxW6fLGwdib5JElqZiGdwYW2kiRtIuntZEPx657k9fWHqaQb/3rRDnNHkt13zQDjEy+jwAgwPPHrIDAEDN753jsdoZQkaRose5IkSZLUgmZy6rUkSZIkqcFZ9iRJkiSpBVn2JEmSJKkFWfYkSZIkqQVZ9iRJkiSpBVn2JEmSJKkFWfYkSZIkqQVZ9iRJkiSpBVn2JEmSJKkFWfYkSZIkqQVZ9iRJkiSpBVn2JEmSJKkFWfYkSZIkqQVZ9iRJkiSpBVn2JEmSJKkFWfYkSZIkqQVZ9iRJkiSpBVn2JEmSJKkFWfYkSZIkqQVZ9iRJkiSpBVn2JEmSJKkFWfYkSZIkqQVZ9iRJkiSpBVn2JEmSJKkFWfYkSZIkqQVZ9iRJkiSpBVn2JElNL4SQCyFs9TEthJAJ4f+3d/+gWtVxHMc/X29Zkg1CDRFuLra0BEZjLlIQQZsZEVhbRGDRkC0VJEUtFhi0RA0SLmJg9AeXtvagoKGIShwqoTKvfRueIzxcr0Jw/zz+er3gGc7v3HP4nenyfn7nOaduWGV837WOA4DrWXX3Zs8BAK6qqvYm2dXdx+bGTnT3I3PbDyV5OsmFaeiuJN8l+WvavinJsSRnkryQZHka35PkpyTfT9tLSV7v7rPrcjEAsIGu+JYTABbMhemTqvqguw8kOT+tyO1O8mN3n0xy8vIBVXU0yZHu/mH+RFW1czrXK0lWfttZSV5Mcst6XQgAbCSxB8DCqqpHkxxKcnNVXUqyXFV7MluRezfJjiTPJ/l1lcM/rKoj3f3x3NjvST7NbIXv9iTnprGlJNuSvHaVcwHAdUfsAbDIjmd2i+WuJHcnuTfJV0m+7u6DVfVGZsG2moNJnqyq/Ume6e5z3f1bkjNV9WaSnUneSnJrkpeSnOjuL9f3cgBg44g9ABZWdy9XVWd2y+WrSW7r7qNVdc/0J9szW5lbzR/d/VxV3Z/kgSTvJ0lVPZjkcJKzSfZNx5/NLAy3dfdn63dFALBxxB4A14Mbk9w5t11V9XJmDxq75pPGuvuLFUOfJDnd3Zeq6vEkv3T36elpndvXdNYAsInEHgALq6q2Znbr5v4k387t+qe7D1fVjqra292f/4fT7ktyqKqWk9yR5O+qOpTZ/8S3k3y0RtMHgE0l9gBYZI8lOZ9kT3dfrKqnqqq6+4lp/+4k9yVZGXs1fa7Q3aeSnEqSqjqQ5Ge3bgIwIrEHwMLq7vdWDH2T5PT02oWlJFszexDLStuyyisUqurhJM8m+XMaml/Zu3zcO919fA2mDwCbykvVAfjfmH6X19196Sr7tyRZ6u6LGzszAFh7Yg8AAGBAWzZ7AgAAAKw9sQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADCgfwEXeBVHXUuiDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 调用plt，作饼图\n",
    "# plt.pie（数值，数值的标签，百分比，开始位置）\n",
    "plt.pie(lianjia_floor_count,labels=lianjia_floor_index,autopct=\"%.2f%%\",startangle=90)\n",
    "\n",
    "# 设置图片标题\n",
    "plt.title(\"链家各种楼层房源分布情况\")\n",
    "# 设置图片大小\n",
    "plt.rcParams['figure.figsize'] = (25,15)\n",
    "# 保存图片为\n",
    "plt.savefig(\"pie.png\")\n",
    "\n",
    "# 展示图片\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 【结论】高楼层出租房子占比最多，接近50%"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
